Epileptic EEG signal classification using optimum allocation based power spectral density estimation

Al Ghayab, Hadi Ratham and Li, Yan and Siuly, Siuly and Abdulla, Shahab (2018) Epileptic EEG signal classification using optimum allocation based power spectral density estimation. IET Signal Processing, 12 (6). pp. 738-747. ISSN 1751-9675

Abstract

Abstract: This study proposes a novel approach blending optimum allocation (OA) technique and spectral density estimation to analyse and classify epileptic electroencephalogram (EEG) signals. This study employs the OA to determine representative sample points from the original EEG data and then applies periodogram (PD), autoregressive (AR), and the mixture of PD and AR to extract the discriminative features from each OA sample group. The obtained feature sets are evaluated by three popular machine learning methods: support vector machine (SVM), quadratic discriminant analysis (QDA), and k-nearest neighbour (kNN). Several output coding approaches of the SVM classifier are tested for selecting the best feature sets. This scheme was implemented on a benchmark epileptic EEG database for evaluation and also compared with existing methods. The experimental results show that the OA_AR feature set yields better performances by the SVM with an overall accuracy of 100%, and outperforms the state-of-the-art works with a 14.1% improvement. Thus, the findings of this study prove that the proposed OA-based AR scheme has significant potential to extract features from EEG signals. The proposed method will assist experts to automatically analyse a large volume of EEG data and benefit epilepsy research.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 11 Feb 2019 06:03
Last Modified: 11 Feb 2019 21:23
Uncontrolled Keywords: Electroencephalogram (EEG); optimum allocation technique; power spectral density estimation method; support vector machine; quadratic discriminant analysis; k-nearest neighbor
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
08 Information and Computing Sciences > 0803 Computer Software > 080301 Bioinformatics Software
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity
17 Psychology and Cognitive Sciences > 1702 Cognitive Sciences > 170203 Knowledge Representation and Machine Learning
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1049/iet-spr.2017.0140
URI: http://eprints.usq.edu.au/id/eprint/33855

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